Overview

Dataset statistics

Number of variables8
Number of observations8010
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory532.0 KiB
Average record size in memory68.0 B

Variable types

Categorical4
Text1
Numeric3

Dataset

Description국민건강보험가입자 및 의료급여수급권자 중 고혈압 또는 당뇨병 환자의 비율○ 시도 및 시군구: 전국 17개 시도의 시군구별 구분- 연초 기준 지명 구축. 연중에 지명이 바뀌는 경우 거주자 중 지명 변경 후 정보 이동이 있었던 일부만 반영○ 성별: ALL(전체), 1(남성), 2(여성)○ 연령그룹: ALL(전체), 00(10세미만), 10(10대), 20(20대), 30(30대), 40(40대), 50(50대), 60(60대), 70(70대), 80(80세이상)○ 분모(명): 국민건강보험가입자 또는 의료급여수급권자 수○ 분자(명): 고혈압 또는 당뇨병이 발생한 환자 수- 고혈압 환자: 고혈압 주상병 코드(I10~I15)와 함께 고혈압 약제를 처방 받은 환자- 당뇨병 환자: 당뇨병 주상병 코드(E10~E14)와 함께 당뇨병 약제를 처방 받은 환자○ 고당의료이용률 = 분자/분모*100(%)※ 분모가 20 미만인 행은 정보 제공 불가
Author국민건강보험공단
URLhttps://www.data.go.kr/data/15119863/fileData.do

Alerts

기준연도 has constant value ""Constant
분모(명) is highly overall correlated with 분자(명)High correlation
분자(명) is highly overall correlated with 분모(명) and 1 other fieldsHigh correlation
고당의료이용률 is highly overall correlated with 분자(명) and 1 other fieldsHigh correlation
연령그룹 is highly overall correlated with 고당의료이용률High correlation
분모(명) is highly skewed (γ1 = 44.2784302)Skewed
분자(명) is highly skewed (γ1 = 41.34779685)Skewed
분자(명) has 116 (1.4%) zerosZeros
고당의료이용률 has 116 (1.4%) zerosZeros

Reproduction

Analysis started2024-04-17 09:55:57.944417
Analysis finished2024-04-17 09:55:59.736307
Duration1.79 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.7 KiB
2022
8010 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 8010
100.0%

Length

2024-04-17T18:55:59.793611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T18:55:59.888092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 8010
100.0%

시도
Categorical

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size62.7 KiB
경기도
1290 
서울특별시
780 
경상북도
750 
경상남도
690 
전라남도
690 
Other values (13)
3810 

Length

Max length7
Median length5
Mean length4.1086142
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전국
2nd row전국
3rd row전국
4th row전국
5th row전국

Common Values

ValueCountFrequency (%)
경기도 1290
16.1%
서울특별시 780
9.7%
경상북도 750
9.4%
경상남도 690
8.6%
전라남도 690
8.6%
강원도 570
7.1%
부산광역시 510
 
6.4%
충청남도 510
 
6.4%
전라북도 480
 
6.0%
충청북도 450
 
5.6%
Other values (8) 1290
16.1%

Length

2024-04-17T18:55:59.990461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 1290
16.1%
서울특별시 780
9.7%
경상북도 750
9.4%
경상남도 690
8.6%
전라남도 690
8.6%
강원도 570
7.1%
부산광역시 510
 
6.4%
충청남도 510
 
6.4%
전라북도 480
 
6.0%
충청북도 450
 
5.6%
Other values (8) 1290
16.1%

시군구
Text

Distinct229
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size62.7 KiB
2024-04-17T18:56:00.302757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.3745318
Min length2

Characters and Unicode

Total characters27030
Distinct characters144
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전체
2nd row전체
3rd row전체
4th row전체
5th row전체
ValueCountFrequency (%)
전체 510
 
5.7%
중구 180
 
2.0%
동구 180
 
2.0%
서구 150
 
1.7%
북구 150
 
1.7%
창원시 150
 
1.7%
남구 150
 
1.7%
수원시 120
 
1.3%
청주시 120
 
1.3%
고양시 90
 
1.0%
Other values (228) 7170
79.9%
2024-04-17T18:56:00.742569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 3180
 
11.8%
시 3000
 
11.1%
군 2550
 
9.4%
960
 
3.6%
주 720
 
2.7%
천 690
 
2.6%
산 690
 
2.6%
양 660
 
2.4%
성 630
 
2.3%
동 600
 
2.2%
Other values (134) 13350
49.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 26070
96.4%
Space Separator 960
 
3.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3180
 
12.2%
시 3000
 
11.5%
군 2550
 
9.8%
주 720
 
2.8%
천 690
 
2.6%
산 690
 
2.6%
양 660
 
2.5%
성 630
 
2.4%
동 600
 
2.3%
전 570
 
2.2%
Other values (133) 12780
49.0%
Space Separator
ValueCountFrequency (%)
960
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 26070
96.4%
Common 960
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3180
 
12.2%
시 3000
 
11.5%
군 2550
 
9.8%
주 720
 
2.8%
천 690
 
2.6%
산 690
 
2.6%
양 660
 
2.5%
성 630
 
2.4%
동 600
 
2.3%
전 570
 
2.2%
Other values (133) 12780
49.0%
Common
ValueCountFrequency (%)
960
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 26070
96.4%
ASCII 960
 
3.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 3180
 
12.2%
시 3000
 
11.5%
군 2550
 
9.8%
주 720
 
2.8%
천 690
 
2.6%
산 690
 
2.6%
양 660
 
2.5%
성 630
 
2.4%
동 600
 
2.3%
전 570
 
2.2%
Other values (133) 12780
49.0%
ASCII
ValueCountFrequency (%)
960
100.0%

성별
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.7 KiB
1
2670 
2
2670 
ALL
2670 

Length

Max length3
Median length1
Mean length1.6666667
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2670
33.3%
2 2670
33.3%
ALL 2670
33.3%

Length

2024-04-17T18:56:00.884681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T18:56:00.989378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2670
33.3%
2 2670
33.3%
all 2670
33.3%

연령그룹
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size62.7 KiB
0
801 
10
801 
20
801 
30
801 
40
801 
Other values (5)
4005 

Length

Max length3
Median length2
Mean length2
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row10
3rd row20
4th row30
5th row40

Common Values

ValueCountFrequency (%)
0 801
10.0%
10 801
10.0%
20 801
10.0%
30 801
10.0%
40 801
10.0%
50 801
10.0%
60 801
10.0%
70 801
10.0%
80 801
10.0%
ALL 801
10.0%

Length

2024-04-17T18:56:01.098067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T18:56:01.236064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 801
10.0%
10 801
10.0%
20 801
10.0%
30 801
10.0%
40 801
10.0%
50 801
10.0%
60 801
10.0%
70 801
10.0%
80 801
10.0%
all 801
10.0%

분모(명)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct7245
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77560.697
Minimum158
Maximum51893314
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.5 KiB
2024-04-17T18:56:01.374038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum158
5-th percentile1183.45
Q14524.25
median12779
Q331581.5
95-th percentile203008.65
Maximum51893314
Range51893156
Interquartile range (IQR)27057.25

Descriptive statistics

Standard deviation799486.49
Coefficient of variation (CV)10.307882
Kurtosis2496.6017
Mean77560.697
Median Absolute Deviation (MAD)9918
Skewness44.27843
Sum6.2126118 × 108
Variance6.3917865 × 1011
MonotonicityNot monotonic
2024-04-17T18:56:01.511870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1809 5
 
0.1%
1047 4
 
< 0.1%
2696 4
 
< 0.1%
1666 4
 
< 0.1%
1380 4
 
< 0.1%
8944 4
 
< 0.1%
4639 4
 
< 0.1%
1301 3
 
< 0.1%
854 3
 
< 0.1%
1083 3
 
< 0.1%
Other values (7235) 7972
99.5%
ValueCountFrequency (%)
158 1
< 0.1%
175 1
< 0.1%
216 1
< 0.1%
220 1
< 0.1%
242 1
< 0.1%
251 1
< 0.1%
268 1
< 0.1%
276 1
< 0.1%
282 1
< 0.1%
290 2
< 0.1%
ValueCountFrequency (%)
51893314 1
< 0.1%
25994398 1
< 0.1%
25898916 1
< 0.1%
13756986 1
< 0.1%
9640552 1
< 0.1%
8723077 1
< 0.1%
8088750 1
< 0.1%
7465685 1
< 0.1%
6944223 1
< 0.1%
6812763 1
< 0.1%

분자(명)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct4917
Distinct (%)61.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18785.269
Minimum0
Maximum12560064
Zeros116
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size70.5 KiB
2024-04-17T18:56:01.633765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q1128
median2119
Q38316
95-th percentile47292.9
Maximum12560064
Range12560064
Interquartile range (IQR)8188

Descriptive statistics

Standard deviation199875.3
Coefficient of variation (CV)10.640002
Kurtosis2215.2524
Mean18785.269
Median Absolute Deviation (MAD)2103
Skewness41.347797
Sum1.5047001 × 108
Variance3.9950137 × 1010
MonotonicityNot monotonic
2024-04-17T18:56:01.763670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 116
 
1.4%
2 76
 
0.9%
1 74
 
0.9%
5 72
 
0.9%
3 68
 
0.8%
7 57
 
0.7%
6 56
 
0.7%
9 55
 
0.7%
4 53
 
0.7%
10 44
 
0.5%
Other values (4907) 7339
91.6%
ValueCountFrequency (%)
0 116
1.4%
1 74
0.9%
2 76
0.9%
3 68
0.8%
4 53
0.7%
5 72
0.9%
6 56
0.7%
7 57
0.7%
8 41
 
0.5%
9 55
0.7%
ValueCountFrequency (%)
12560064 1
< 0.1%
6600964 1
< 0.1%
5959100 1
< 0.1%
3746731 1
< 0.1%
3077751 1
< 0.1%
2694042 1
< 0.1%
2651913 1
< 0.1%
2158963 1
< 0.1%
2027500 1
< 0.1%
1852924 1
< 0.1%

고당의료이용률
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3980
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.777829
Minimum0
Maximum85.58
Zeros116
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size70.5 KiB
2024-04-17T18:56:01.887249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.09
Q11.35
median20.705
Q351.505
95-th percentile75.7855
Maximum85.58
Range85.58
Interquartile range (IQR)50.155

Descriptive statistics

Standard deviation27.471351
Coefficient of variation (CV)0.98896683
Kurtosis-1.1391334
Mean27.777829
Median Absolute Deviation (MAD)20.035
Skewness0.58157175
Sum222500.41
Variance754.67514
MonotonicityNot monotonic
2024-04-17T18:56:02.007951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 116
 
1.4%
0.08 85
 
1.1%
0.11 70
 
0.9%
0.1 67
 
0.8%
0.09 65
 
0.8%
0.07 62
 
0.8%
0.12 50
 
0.6%
0.06 43
 
0.5%
0.05 41
 
0.5%
0.38 38
 
0.5%
Other values (3970) 7373
92.0%
ValueCountFrequency (%)
0.0 116
1.4%
0.03 13
 
0.2%
0.04 26
 
0.3%
0.05 41
 
0.5%
0.06 43
 
0.5%
0.07 62
0.8%
0.08 85
1.1%
0.09 65
0.8%
0.1 67
0.8%
0.11 70
0.9%
ValueCountFrequency (%)
85.58 1
< 0.1%
84.81 1
< 0.1%
83.74 1
< 0.1%
83.08 1
< 0.1%
83.07 1
< 0.1%
82.9 1
< 0.1%
82.73 1
< 0.1%
82.54 1
< 0.1%
82.52 1
< 0.1%
82.39 1
< 0.1%

Interactions

2024-04-17T18:55:58.990738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:55:58.462891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:55:58.714098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:55:59.087897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:55:58.541788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:55:58.804560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:55:59.479695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:55:58.627461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:55:58.896348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T18:56:02.094258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도성별연령그룹분모(명)분자(명)고당의료이용률
시도1.0000.0000.0000.4520.5160.151
성별0.0001.0000.0000.0400.0400.371
연령그룹0.0000.0001.0000.0070.0020.957
분모(명)0.4520.0400.0071.0000.9890.018
분자(명)0.5160.0400.0020.9891.0000.026
고당의료이용률0.1510.3710.9570.0180.0261.000
2024-04-17T18:56:02.200632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별연령그룹시도
성별1.0000.0000.000
연령그룹0.0001.0000.000
시도0.0000.0001.000
2024-04-17T18:56:02.292615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분모(명)분자(명)고당의료이용률시도성별연령그룹
분모(명)1.0000.6410.0330.1970.0160.004
분자(명)0.6411.0000.7320.2320.0160.001
고당의료이용률0.0330.7321.0000.0580.2400.645
시도0.1970.2320.0581.0000.0000.000
성별0.0160.0160.2400.0001.0000.000
연령그룹0.0040.0010.6450.0000.0001.000

Missing values

2024-04-17T18:55:59.578585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T18:55:59.684929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

기준연도시도시군구성별연령그룹분모(명)분자(명)고당의료이용률
02022전국전체10174904116560.09
12022전국전체1102389691105470.44
22022전국전체1203400939553391.63
32022전국전체13035418642265676.4
42022전국전체140411668082577720.06
52022전국전체1504405905163953537.21
62022전국전체1603677918202750055.13
72022전국전체1701770238120216967.91
82022전국전체18084664061187472.27
92022전국전체1ALL25898916660096425.49
기준연도시도시군구성별연령그룹분모(명)분자(명)고당의료이용률
80002022제주특별자치도서귀포시ALL012631100.08
80012022제주특별자치도서귀포시ALL1017769870.49
80022022제주특별자치도서귀포시ALL20191392921.53
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